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Generative AI courses

911 Courses

California Institute of Technology Coursera

Generative AI for Consultants

Generative AI for Consultants Enroll in the Generative AI for Consultants course on Coursera to master applying Generative AI in various industries. This course will equip you with essential skills to integrate Gen AI solutions, effectively address complex challenges, and deliver substantial value to your clients through cutting-edge approaches.
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California Institute of Technology Coursera

Generative AI for Data Engineers

Generative AI for Data Engineers Data Engineering is all about efficient data collection, generation, transformation, and storage. Generative AI tools have the capability of making each of these data engineering tasks more efficient, effective, and convenient on an ETL pipeline. This specialization is designed not only for Data Engineers but als.
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California Institute of Technology Coursera

Generative AI in Marketing

Generative AI in Marketing | Coursera Specialization Marketing is an important application of Generative AI for creating personalized and targeted marketing campaigns that help you stay ahead of the competition. In today's landscape, customers expect more personalized and engaging experiences from brands. This specialization introduces the cor.
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California Institute of Technology Coursera

Generative AI for Product Managers

Be it task automation or user experience personalization, generative AI is revolutionizing how we design and develop products. This specialization will help Product Managers, new and experienced, to become well-versed with generative AI and gain insights on how to leverage this technology to their advantage. The specialization begins with how.
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California Institute of Technology Coursera

GenAI for Marketing Analysts: Innovate Marketing Strategies

GenAI for Marketing Analysts: Innovate Marketing Strategies This in-depth course is designed to explore the transformative impact of GenAI on marketing data analytics. Gain a comprehensive understanding of leveraging GenAI to enhance various marketing functions, including campaign optimization, customer segmentation, sentiment analytics, and pe.
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California Institute of Technology Pluralsight

Generative AI Foundations: Getting Started

Generative AI Foundations: Getting Started In this fast-paced, pragmatic introduction, quickly ramp up on the current state of generative AI, including how to get better results from tools like ChatGPT, Anthropic Claude, and Google Bard. In this fast-paced, short course, you will quickly cover the most important concepts, ideas, and practical a.
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California Institute of Technology Pluralsight

Generative AI Foundations: Prompt Engineering

Generative AI Foundations: Prompt Engineering Generative AI Foundations: Prompt Engineering Master the art of crafting effective prompts for chatbots and generative AI. Learn techniques to get better results by tailoring prompts using attributes like length, style, perspective, and examples to guide any model. In this course, Gener.
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California Institute of Technology Codecademy

Intro to LLMs

Intro to LLMs - Learn How Large Language Models Work | Codecademy Learn how large language models (LLMs) work, how they're used, and what their adjustable parameters do. Large Language Models (LLMs) and text generation are at the heart of many cutting-edge AI applications today. This course is a no-code introduction to LLMs, covering their hist.
California Institute of Technology Coursera

Intro to Snowflake for Devs, Data Scientists, Data Engineers

Intro to Snowflake for Devs, Data Scientists, Data Engineers Unlock the potential of Snowflake with our comprehensive course designed for developers, data scientists, and data engineers. This course introduces you to Snowflake as a powerful platform for building applications, creating data pipelines, and developing AI models and workflows. You'll.
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California Institute of Technology AWS Skill Builder

Building a Generative AI-Ready Organization

Building a Generative AI-Ready Organization Building a Generative AI-Ready Organization is the final course in the three-part Generative AI Essentials series for Business and Technical Decision Makers. If you haven't yet, start with the first course, Introduction to Generative AI: Art of the Possible. By the end of this 1-hour b.
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A generative ai course is a fast-growing field of machine learning that can create new content, translate languages, write different types of creative content, and answer your questions in an informative way. It has great potential to revolutionize the way we create and use products.

A generative ai course refers to any artificial intelligence model that generates new data, information, or documents.

For example, many companies record their meetings, both live and virtual. Here are a few ways generative AI could transform these recordings:

And this is only a small part of all processes.

Generative AI Model Examples

There are a number of products using generative ai courses already available on the market – we'll give you a few examples below. The underlying principle of the generative ai courses at AI Eeducation varies depending on the specific model or algorithm used, but some common approaches include:

  1. Variational Autoencoders (VAEs) are a type of generative model that learns to encode input data into a latent space and then decode it back into the original data. The "variational" part of the name refers to the probabilistic nature of the latent space, allowing the model to generate a variety of outputs.

  2. Generative Adversarial Networks (GaN): GaNs consist of two neural networks, a generator and a discriminator, that are trained simultaneously through adversarial learning. The generator creates new data, and the discriminator evaluates how well the generated data matches the real data. The competition between the two networks causes the generator to improve over time in producing realistic outputs.

  3. Recurrent Neural Networks (RNNS) and Long Short-Term Memory (LSTM): These types of neural networks are often used to generate sequences such as text or music. RNNS and LSTM have memory that allows them to process a series of events over time, making them suitable for tasks where the order of elements is important.

  4. Transformer models: Transformer models, especially those with attention mechanisms, are very successful in various generative tasks. They can remember long-term dependencies and relationships in data, making them effective for tasks such as language translation and text generation.

  5. Autoencoders: Autoencoders consist of an encoder and a decoder, and they are trained to reconstruct the input data. Although they are primarily used for learning to represent and compress data, variations such as denoising autoencoders (e.g. in images) can be used for generative tasks.

An ai generative course involves feeding a model a large data set and optimizing its parameters to minimize the difference between the generated output and the real information. A model's ability to produce realistic and rich content depends on the complexity of its architecture, the quality and quantity of training data, and the optimization techniques used during training!